The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Infomax and maximum likelihood for blind source separation
Abstract
Algorithms for the blind separation of sources can be derived from several different principles. This article shows that the infomax (information-maximization) principle is equivalent to the maximum likelihood. The application of the infomax principle to source separation consists of maximizing an output entropy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Cited by
Lee, T.-W., Girolami, M., & Sejnowski, T. J. (1999) · Neural Computation · doi:10.1162/089976699300016719 · Cited by 1,967
Adaptive blind signal processing-neural network approaches
Amari, S., & Cichocki, A. (1998) · Proceedings of the IEEE · doi:10.1109/5.720251 · Cited by 466
EEG source imaging enhances the decoding of complex right-hand motor imagery tasks
Edelman, B. J., Baxter, B., & He, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2467312 · Cited by 370
A unifying information-theoretic framework for independent component analysis
Lee, T.-W., Girolami, M., Bell, A. J., & Sejnowski, T. J. (2000) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(00)00101-2 · Cited by 355
Calhoun, V. D., Adali, T., Stevens, M. C., Kiehl, K. A., & Pekar, J. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.012 · Cited by 159
Metadata
- Authors
- Cardoso, J.-F.
- Venue
- IEEE Signal Processing Letters
- Published
- 1997-04
- Volume
- 4
- Issue
- 4
- Pages
- 112-114
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2143132653
- Cited by
- 684
- References
- 1
- FWCI
- 32.64
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Infomax and maximum likelihood for blind source separation. https://app.cochat.ai/papers/10.1109/97.566704
- The paper
- Cardoso, J.-F. (1997). Infomax and maximum likelihood for blind source separation. IEEE Signal Processing Letters. https://doi.org/10.1109/97.566704
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
